the relative impacts of different types of land use on the surface water quality are yet to be ascertained and quantified. in this paper, the influence of different types of land use on surface water quality is investigated. rain events samples from different land use in the central plateau, iran, were analyzed for major ions. statistical analyses were employed to examine the statistical relati...

In this paper, we present a representation for three-dimensional geometric animation sequences. Different from standard key-frame techniques, this approach is based on the determination of principal animation components and decouples the animation from the underlying geometry. The new representation supports progressive animation compression with spatial, as well as temporal, level-of-detail an...

Canonical Correlation is one of the most general of the multivariate techniques. It is used to investigate the overall correlation between two sets of variables (p’ and q’). The basic principle behind canonical correlation is determining how much variance in one set of variables is accounted for by the other set along one or more axes. If there is more than one axis, they must be orthogonal. Un...

In family studies with multiple continuous phenotypes, heritability can be conveniently evaluated through the so-called principal-component of heredity (PCH, for short; Ott and Rabinowitz in Hum Hered 49:106–111, 1999). Estimation of the PCH, however, is notoriously difficult when entertaining a large collection of phenotypes which naturally arises in dealing with modern genomic data such as th...

In this paper, we discuss the extension to the functional setting of the common principal component model that has been widely studied when dealing with multivariate observations. We provide estimators of the common eigenfunctions and study their asymptotic behavior.

This work proposes an extension of the functional principal components analysis, or Karhunen-Loève expansion, which can take into account non-parametrically the effects of an additional covariate. Such models can also be interpreted as non-parametric mixed effects models for functional data. We propose estimators based on kernel smoothers and a data-driven selection procedure of the smoothing p...

Many frequently arising problems involve finding the small-dimensional subspace that best captures the variablity of a set of observations belonging to a larger space, for example, finding its principal components. We propose an algorithm that finds this subspace through a series of orthogonal rotations, each represented as the exponential of a skew-symmetric matrix picked such that we minimize...

This paper deals with Principal Components Analysis (PCA) of data spread over a network where central coordination and synchronous communication between networking nodes are forbidden. We propose an asynchronous and decentralized PCA algorithm dedicated to large scale problems, where ”large” simultaneously applies to dimensionality, number of observations and network size. It is based on the in...

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction prop...